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"""This module contains transforms for videos."""
import numbers
import random
import numpy as np
from torchvision.transforms import RandomResizedCrop
from . import functional_video as F
__all__ = [
"RandomResizedCropVideo",
"CenterCropVideo",
"NormalizeVideo",
"ToTensorVideo",
"RandomHorizontalFlipVideo",
]
class ResizeVideo:
def __init__(self, size, interpolation_mode="bilinear"):
self.size = size
self.interpolation_mode = interpolation_mode
def __call__(self, clip):
return F.resize(clip, self.size, self.interpolation_mode)
class RandomResizedCropVideo(RandomResizedCrop):
def __init__(
self,
size,
crop,
interpolation_mode="bilinear",
):
if isinstance(size, tuple):
assert len(size) == 2, "size should be tuple (height, width)"
self.size = size
else:
self.size = (size, size)
self.interpolation_mode = interpolation_mode
self.crop = crop
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
Returns:
torch.tensor: randomly cropped/resized video clip.
size is (C, T, H, W)
"""
clip = F.resize(clip, self.size, self.interpolation_mode)
# print(clip.shape)
if clip.shape[2] - self.crop > 0:
i = np.random.randint(clip.shape[2] - self.crop)
else:
i = 0
if clip.shape[3] - self.crop > 0:
j = np.random.randint(clip.shape[3] - self.crop)
else:
j = 0
clip = clip[..., i : i + self.crop, j : j + self.crop]
return clip
def __repr__(self):
return (
self.__class__.__name__
+ f"(size={self.size}, interpolation_mode={self.interpolation_mode}, "
+ f"scale={self.scale}, ratio={self.ratio})"
)
class CenterCropVideo:
def __init__(self, crop_size):
if isinstance(crop_size, numbers.Number):
self.crop_size = (int(crop_size), int(crop_size))
else:
self.crop_size = crop_size
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
Returns:
torch.tensor: central cropping of video clip. Size is
(C, T, crop_size, crop_size)
"""
return F.center_crop(clip, self.crop_size)
def __repr__(self):
return self.__class__.__name__ + f"(crop_size={self.crop_size})"
class NormalizeVideo:
"""
Normalize the video clip by mean subtraction and division by standard deviation
Args:
mean (3-tuple): pixel RGB mean
std (3-tuple): pixel RGB standard deviation
inplace (boolean): whether do in-place normalization
"""
def __init__(self, mean, std, inplace=False):
self.mean = mean
self.std = std
self.inplace = inplace
def __call__(self, clip):
"""
Args:
clip (torch.tensor): video clip to be normalized. Size is (C, T, H, W)
"""
return F.normalize(clip, self.mean, self.std, self.inplace)
def __repr__(self):
return (
self.__class__.__name__
+ f"(mean={self.mean}, std={self.std}, inplace={self.inplace})"
)
class ToTensorVideo:
"""Convert tensor data type from uint8 to float, divide value by 255.0 and
permute the dimenions of clip tensor."""
def __init__(self):
pass
def __call__(self, clip):
"""
Args:
clip (torch.tensor, dtype=torch.uint8): Size is (T, H, W, C)
Return:
clip (torch.tensor, dtype=torch.float): Size is (C, T, H, W)
"""
return F.to_tensor(clip)
def __repr__(self):
return self.__class__.__name__
class RandomHorizontalFlipVideo:
"""
Flip the video clip along the horizonal direction with a given probability
Args:
p (float): probability of the clip being flipped. Default value is 0.5
"""
def __init__(self, p=0.5):
self.p = p
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Size is (C, T, H, W)
Return:
clip (torch.tensor): Size is (C, T, H, W)
"""
if random.random() < self.p:
clip = F.hflip(clip)
return clip
def __repr__(self):
return self.__class__.__name__ + f"(p={self.p})"
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